A Laptop Ensemble Performance System using Recurrent Neural Networks
This work provides an assistance tool for laptop ensemble performers, enabling musical novices to engage with experienced performers and expanding creative expression for all users, though it is an incremental step in generative music and NIME.
This paper presents a laptop ensemble performance system that uses pre-trained recurrent neural networks to assist live improvisational performances. The system was tested in two performances, successfully supporting four performers over a four-minute piece and producing musically cohesive music.
The popularity of applying machine learning techniques in musical domains has created an inherent availability of freely accessible pre-trained neural network (NN) models ready for use in creative applications. This work outlines the implementation of one such application in the form of an assistance tool designed for live improvisational performances by laptop ensembles. The primary intention was to leverage off-the-shelf pre-trained NN models as a basis for assisting individual performers either as musical novices looking to engage with more experienced performers or as a tool to expand musical possibilities through new forms of creative expression. The system expands upon a variety of ideas found in different research areas including new interfaces for musical expression, generative music and group performance to produce a networked performance solution served via a web-browser interface. The final implementation of the system offers performers a mixture of high and low-level controls to influence the shape of sequences of notes output by locally run NN models in real time, also allowing performers to define their level of engagement with the assisting generative models. Two test performances were played, with the system shown to feasibly support four performers over a four minute piece while producing musically cohesive and engaging music. Iterations on the design of the system exposed technical constraints on the use of a JavaScript environment for generative models in a live music context, largely derived from inescapable processing overheads.